Hadoop

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.


References in zbMATH (referenced in 52 articles )

Showing results 1 to 20 of 52.
Sorted by year (citations)

1 2 3 next

  1. Fuerst, Carlo; Pacut, Maciej; Schmid, Stefan: Data locality and replica aware virtual cluster embeddings (2017)
  2. Gong, Xueqing; Sung, Chi Wan: Zigzag decodable codes: linear-time erasure codes with applications to data storage (2017)
  3. Lanza, Daniel; Chávez, F.; Fernandez, Francisco; Garcia-Valdez, M.; Trujillo, Leonardo; Olague, Gustavo: Profiting from several recommendation algorithms using a scalable approach (2017)
  4. Lefticaru, Raluca; Macías-Ramos, Luis F.; Niculescu, Ionuţ Mihai; Mierlă, Laurenţiu: Agent-based simulation of kernel P systems with division rules using FLAME (2017)
  5. Luo, Taibo; Zhu, Yuqing; Wu, Weili; Xu, Yinfeng; Du, Ding-Zhu: Online makespan minimization in MapReduce-like systems with complex reduce tasks (2017)
  6. Bermanis, Amit; Salhov, Moshe; Wolf, Guy; Averbuch, Amir: Measure-based diffusion grid construction and high-dimensional data discretization (2016)
  7. Choi, Woohyuk; Hong, Sumin; Jeong, Won-Ki: Vispark: GPU-accelerated distributed visual computing using Spark (2016)
  8. Derbeko, Philip; Dolev, Shlomi; Gudes, Ehud; Sharma, Shantanu: Security and privacy aspects in MapReduce on clouds: a survey (2016)
  9. Iwen, M.A.; Ong, B.W.: A distributed and incremental SVD algorithm for agglomerative data analysis on large networks (2016)
  10. Rizk, Amr; Poloczek, Felix; Ciucu, Florin: Stochastic bounds in Fork-Join queueing systems under full and partial mapping (2016)
  11. Steele, Brian; Chandler, John; Reddy, Swarna: Algorithms for data science (2016)
  12. Yang, Chao-Tung; Shih, Wen-Chung; Huang, Chih-Lin; Jiang, Fuu-Cheng; Chu, William Cheng-Chung: On construction of a distributed data storage system in cloud (2016) ioport
  13. Zhao, Jiaqi; Tao, Jie; Streit, Achim: Enabling collaborative MapReduce on the cloud with a single-sign-on mechanism (2016) ioport
  14. Giachetta, Roberto; Fekete, István: A case study of advancing remote sensing image analysis (2015) ioport
  15. Green, Peter J.; Łatuszyński, Krzysztof; Pereyra, Marcelo; Robert, Christian P.: Bayesian computation: a summary of the current state, and samples backwards and forwards (2015)
  16. Hutchinson, M.; Widom, M.: Enumeration of octagonal tilings (2015)
  17. Kacfah Emani, Cheikh; Cullot, Nadine; Nicolle, Christophe: Understandable big data: a survey (2015) ioport
  18. López, Victoria; del Río, Sara; Benítez, José Manuel; Herrera, Francisco: Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data (2015) ioport
  19. Luo, Chen; He, Fei; Ghezzi, Carlo: Inferring software behavioral models with MapReduce (2015)
  20. Neven, Frank; Schweikardt, Nicole; Servais, Frédéric; Tan, Tony: Distributed streaming with finite memory (2015)

1 2 3 next